Typical signal processing systems process large amounts of data representing the signal energy that is present in an RF environment of interest. Frequently, the amount of data is so large that it exceeds the system resources available to process the data. In these circumstances, meaningful data is often lost. One approach to dealing with this problem is to add more system resources. This approach is often undesirable, however, due to cost constraints. Another approach is to reduce data throughput. Typical signal processing systems reduce data throughput by throttling the amount of data that is allowed through the system by the use of data throttling queues. Basically, data is allowed through the system until the queue fills up. Once the queue fills with data, any additional data is dropped (i.e., prevented from continuing to pass through the system), thereby reducing data throughput.
An undesirable consequence of these types of throttling systems is that the data that is lost is often relevant data (i.e., it includes information of interest to the signal processing system). In certain applications, however, it is known that much of the signal data that passes through the system includes redundant information. Designers of signal processing systems, therefore, would benefit from methods and apparatus that reduce data throughput by blocking redundant data while allowing relevant data to pass.